Evaluating Subspace Search Methods with Hidden Outlier

In today’s world, most datasets do not have only a small number of attributes. The high

number of attributes, which are referred to as dimensions, hinder the search of objects
that normally not occur. For instance, consider a money transaction that has been not
legally carried out. Such objects are called outlier. A common method to detect outliers
in high dimensional datasets are based on the search in subspaces of the dataset. These
subspaces have the characteristics to reveal possible outliers. The most common evaluation of algorithms searching for subspaces is based on benchmark datasets. However, the
benchmark datasets are often not suitable for the evaluation of these subspace search algorithms. In this context, we present a method that evaluates subspace search algorithms
without relying on benchmark datasets by hiding outliers in the result set of a subspace
search algorithm.